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At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Transmitter with fully re-assignable segments for reconfigurable FFE taps

Methods and systems of performing feed forward equalization (FFE) on data streams are described. A circuitry may generate staggered data streams from data streams of an input signal. The staggered data streams may include data in staggered unit intervals. The circuitry may include a plurality of segments. A segment may define a specific unit interval to carve the staggered data streams into one unit interval pulses positioned at the specific unit interval. The specific unit interval to carve the staggered data streams may indicate an assignment of the segment as one of a FFE pre tap, a FFE main tap, and a FFE post tap. The plurality of segments may be assigned to different FFE taps based on different clock signal selection defining different unit intervals to perform the carving. The plurality of segments may output respective one unit interval pulses to reproduce the input signal.

Toprak-Deniz, Zeynep↗
DOE OSTI2024

Systems and methods for dynamically reconfigurable artificial synapses and neurons with tunable activation functions

An ionic redox transistor comprises a solid channel, a solid reservoir layer, and a solid electrolyte layer disposed between the channel and the reservoir layer. The channel exhibits a substantially linear current-voltage relationship in a first range of voltages, and a nonlinear current-voltage relationship in a second range of voltages that is greater than the first range of voltages. One or both of the substantially linear current-voltage relationship or the nonlinear current-voltage relationship of the channel is varied by changing the concentration of ions such as oxygen vacancies in the channel. Ion or vacancy transport between the channel and the reservoir layer across the electrolyte layer occurs in response to applying a voltage between the channel and the reservoir layer. Subject to the first range of voltages, the channel can function as a synapse device. Subject to the second range of voltages, the channel can function as a neuron device.

Talin, Albert Alec↗
DOE OSTI2025

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

We present hls4ml, a free and open-source platform that translates machine learning (ML) models from modern deep learning frameworks into high-level synthesis (HLS) code that can be integrated into full designs for field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs). With its flexible and modular design, hls4ml supports a large number of deep learning frameworks and can target HLS compilers from several vendors, including Vitis HLS, Intel oneAPI and Catapult HLS. Together with a wider eco-system for software-hardware co-design, hls4ml has enabled the acceleration of ML inference in a wide range of commercial and scientific applications where low latency, resource usage, and power consumption are critical. In this paper, we describe the structure and functionality of the hls4ml platform. The overarching design considerations for the generated HLS code are discussed, together with selected performance results.

Schulte, Jan-Frederik [Purdue U.] (ORCID:000000034↗